It has been reported that the performance of bulk heterojunction organic solar cells can be improved by incorporation of nano-heterostructures of metals, semiconductors, and dielectric materials in ...the active layer. In this manuscript, CdS or Sb
2
S
3
nanocrystals were in situ generated inside the poly(3-hexylthiophene): 6,6-phenyl C61-butyric acid (P3HT:PC
61
BM) system by randomly mixing P3HT and PC
61
BM in the presence of cadmium or antimony xanthate precursor. Hybrid solar cells (HSCs) with the configurations of tin-doped indium oxide substrate (ITO)/CdS interface layer/P3HT:PC
61
BM: x wt.% CdS/MoO
3
/Ag and ITO/CdS interface layer /P3HT:PC
61
BM: x wt.% Sb
2
S
3
/MoO
3
/Ag were fabricated. Hybrid active layers (P3HT:PC
61
BM: x wt.% CdS or P3HT:PC
61
BM: x wt.% Sb
2
S
3
) were formed completely by thermally annealing the film resulting in the decomposition of the cadmium or antimony xanthate precursor to CdS or Sb
2
S
3
nanocrystals, respectively. The effects of x wt.% CdS (or Sb
2
S
3
) nanocrystals on the performance of the HSCs were studied. From UV–Vis absorption, hole mobilities, and surface morphological characterizations, it has been proved that incorporation of 3 wt.% CdS (or Sb
2
S
3
) nanocrystals in the active layer of P3HT:PC
61
BM-based solar cells improved the optical absorption, the hole mobility, and surface roughness in comparison with P3HT:PC
61
BM-based solar cells, thus resulting in the improved power conversion efficiencies (PCEs) of the devices.
With the development of NK cell-directed therapeutic strategies, the actual effect of NK cells on the cellular SIV DNA levels of the virus in SIV-infected macaques in vivo remains unclear. In this ...study, five chronically SIVmac239-infected, treatment-naïve rhesus macaques were euthanized, and the blood, spleen, pararectal/paracolonic lymph nodes (PaLNs), and axillary lymph nodes (ALNs) were collected. The distributional, phenotypic, and functional profiles of NK cells were detected by flow cytometry. The highest frequency of NK cells was found in PBMC, followed by the spleen, while only 0~0.5% were found in LNs. Peripheral NK cells also exhibited higher cytotoxic potential (CD56− CD16+ NK subsets) and IFN-γ-producing capacity but low PD-1 and Tim-3 levels than those in the spleen and LNs. Our results demonstrated a significant positive correlation between the frequency of NK cells and the ratios of cellular SIV DNA/RNA in HLADR− CD4+ T cells (r = 0.6806, p < 0.001) in SIV-infected macaques, despite no discrepancies in the cellular SIV DNA or RNA levels that were found among the blood, spleen, and LNs. These findings showed a profile of NK cell frequencies and NK cytotoxicity levels in different immune organs from chronically SIVmac239-infected, treatment-naïve rhesus macaques. It was suggested that NK cell frequencies could be closely related to SIV DNA/RNA levels, which could affect the transcriptional activity of SIV proviruses. However, the cytotoxicity effect of NK cells on the latent SIV viral load in LNs could be limited due to the sparse abundance of NK cells in LNs. The development of NK cell-directed treatment approaches aiming for HIV clearance remains challenging.
Alzheimer's disease (AD) is one of the most common neurodegenerative disorders, but there is still no effective way to stop or slow its progression. Our previous studies demonstrated that extract of ...Cynomorium songaricum (ECS), a Chinese herbal medicine, had neuroprotective effects in AD models in vivo. However, the pharmacological mechanism of ECS in AD is still unclear.
To study the mechanisms of action of the effects of ECS on AD, we used Aβ
and H
O
-exposed HT22 cells to mimic specific stages of AD in vitro. The mitochondrial membrane potential (MMP), intracellular ATP, intracellular reactive oxygen species (ROS), and expression levels of mitochondrial dynamics-related proteins in each group were examined. Furthermore, we explored the mechanisms by which ECS reduces the phosphorylation of Drp1 at Ser637 and the changes in the concentrations of intracellular calcium ions in the two models after FK506 intervention.
The results showed that ECS significantly enhanced the MMP (P < 0.05), increased intracellular ATP levels (P < 0.05) and decreased intracellular ROS levels in the Aβ- and H
O
-induced cell models (P < 0.05). Additionally, ECS regulated the expression levels of mitochondrial dynamics-related proteins by reducing the phosphorylation of Drp1 at Ser637 (P < 0.05) and decreasing the expression of Fis1 in the H
O
-induced models (P < 0.05). Further study indicated that ECS reduced the overload of intracellular calcium (P < 0.05).
Our study results suggest that ECS protects the mitochondrial ultrastructure, ameliorates mitochondrial dysfunction, and maintains mitochondrial dynamics in AD models.
Accurately defining the level of geomagnetically induced currents (GICs) in a power grid is an important process in evaluating the effects of magnetic storm disturbance on a power grid. With the ...full-node model presented in this paper, the long-term effects of geomagnetic disturbances on the 400-230-kV power grid in Ethiopia are calculated. Two parameters, namely, GICMax-H and GICMax-L (high- and low-probability extreme values), which are the bases of the risk assessment of the GIC, are proposed. The differences and the interaction of the GIC between the 230- and 400-kV power grids in November 9-11, 2004, are presented. The results expose that the probability of a high-risk GIC value in the low-latitude region can still be significant. The low-voltage system should be included when modeling and assessing the GIC in the high-voltage system. In Ethiopia, the GIC values in the northwest, southeast, and central regions are larger, and the 400-kV substations, such as S1, S2, S8, S10, and S48, and the 230-kV substations, such as S42, S43, S17, S16, and S53, should be intensively monitored when the effect of the GIC is evaluated. All the results can enrich the knowledge on the GIC in the low-latitude region and provide some assistance to assess the geomagnetic storm disasters.
Legged robots have shown great adaptability to various environments. However, conventional walking gaits are insufficient to meet the motion requirements of robots. Therefore, achieving high-speed ...running for legged robots has become a significant research topic. In this paper, based on the Spring-Loaded Inverted Pendulum (SLIP) model and the optimized Double leg-Spring-Loaded Inverted Pendulum (D-SLIP) model, the running control strategies for the double flying phase Bound gait and the Rotatory gallop gait of quadruped robots are designed. First, the dynamics of the double flying phase Bound gait and Rotatory gallop gait are analyzed. Then, based on the "three-way" control idea of the SLIP model, the running control strategy for the double flying phase Bound gait is designed. Subsequently, the SLIP model is optimized to derive the D-SLIP model with two touchdown legs, and its dynamic characteristics are analyzed. And the D-SLIP model is applied to the running control strategy of the Rotatory gallop gait. Furthermore, joint simulation verification is conducted using Adams virtual prototyping and MATLAB/Simulink control systems for the designed control strategies. Finally, experimental verification is performed for the double flying phase Bound gait running control strategy. The experimental results demonstrate that the quadruped robot can achieve high-speed and stable running.
5-Methylcytosine (m5C) is a crucial post-transcriptional modification. With the development of technology, it is widely found in various RNAs. Numerous studies have indicated that m5C plays an ...essential role in various activities of organisms, such as tRNA recognition, stabilization of RNA structure, RNA metabolism and so on. Traditional identification is costly and time-consuming by wet biological experiments. Therefore, computational models are commonly used to identify the m5C sites. Due to the vast computing advantages of deep learning, it is feasible to construct the predictive model through deep learning algorithms.
In this study, we construct a model to identify m5C based on a deep fusion approach with an improved residual network. First, sequence features are extracted from the RNA sequences using Kmer, K-tuple nucleotide frequency component (KNFC), Pseudo dinucleotide composition (PseDNC) and Physical and chemical property (PCP). Kmer and KNFC extract information from a statistical point of view. PseDNC and PCP extract information from the physicochemical properties of RNA sequences. Then, two parts of information are fused with new features using bidirectional long- and short-term memory and attention mechanisms, respectively. Immediately after, the fused features are fed into the improved residual network for classification. Finally, 10-fold cross-validation and independent set testing are used to verify the credibility of the model. The results show that the accuracy reaches 91.87%, 95.55%, 92.27% and 95.60% on the training sets and independent test sets of Arabidopsis thaliana and M.musculus, respectively. This is a considerable improvement compared to previous studies and demonstrates the robust performance of our model.
The data and code related to the study are available at https://github.com/alivelxj/m5c-DFRESG.
Xinjiang production and Construction Corps (XPCC) is an important provincial administration in China and vigorously promotes the construction of industrialization. However, there has been little ...research on its emissions. This study first established the 1998-2018 XPCC subsectoral carbon emission inventory based on the Intergovernmental Panel on Climate Change (IPCC) carbon emission inventory method and adopted the logarithmic mean Divisia indexmethod (LMDI) model to analyze the driving factors. The results revealed that from 1998 to 2018, the total carbon emissions in the XPCC increased from 6.11 Mt CO
2
in 1998 to 115.71 Mt CO
2
in 2018. For the energy structure, raw coal, coke and industrial processes were the main contributors to carbon emissions. For industrial structure, the main emission sectors were the production and supply of electric power, steam and hot water, petroleum processing and coking, raw chemical materials and chemical products, and smelting and pressing of nonferrous metals. In addition, the economic effect was the leading factor promoting the growth of the corps carbon emissions, followed by technical and population effects. The energy structure effect was the only factor yielding a low emission reduction degree. This research provides policy recommendations for the XPCC to formulate effective carbon emission reduction measures, which is conducive to the construction of a low-carbon society. Moreover, it is of guiding significance for the development of carbon emission reduction actions for the enterprises under the corps and provides a reference value for other provincial regions.
Images captured under water are often characterized by low contrast, color distortion, and noise, hindering some visual tasks carried out on it. Despite remarkable breakthrough has been made in ...recent years, effective and robust enhancement of degraded image remains a challenging problem. To improve the quality of underwater images, we propose a novel scheme by constructing an adaptive color and contrast enhancement, and denoising (ACCE-D) framework. In the proposed framework, Difference of Gaussian (DoG) filter and bilateral filter are respectively employed to decompose the high-frequency and low-frequency components. Benefited from this separation, we utilize soft-thresholding operation to suppress the noise in the high-frequency component. Specially, the low-frequency component is enhanced by using an adaptive color and contrast enhancement (ACCE) strategy. Moreover, we derive a numerical solution for ACCE, and adopt a pyramid-based strategy to accelerate the solving procedure. Both qualitative and quantitative experiments demonstrate that our strategy is effective in color correction, contrast enhancement, and detail revealing. In the quantitative evaluations, by performing on the 890 real-world underwater images from UIEBD, the proposed method obtains 0.65 UCIQE, 1.59 UIQM, 0.81 FDUM, 1.34 PCQI, 0.62 CBPD, and 7.75 entropy scores, achieving average increase of 5% comparing with several state-of-the-art methods. Furthermore, we have verified the utility of our proposed ACCE-D for enhancing other types of degraded scenes, including foggy scene, sandstorm scene and low-light scene.
In recent years, the water and sediment pattern of the Yellow River has changed significantly, and a preliminary water and sediment regulation system was constructed. Based on a summary of the ...regulation principles of water and sediment in the Middle Yellow River, this paper proposes three key technologies to determine the water and sediment control thresholds, the artificial creation of a long-distance density current, and an engineering regulation for water and sediment control of the Yellow River. Taking the actual flood and sedimentation regulation of the Yellow River Basin in 2018 as an example, the practical applications of these relevant technologies are analyzed. This study provides an important theoretical and practical reference for the flood and sediment regulation of sediment-laden rivers in arid and semi-arid areas during flood season.
Abstract
RNA 5-hydroxymethylcytosine (5hmC) is a kind of RNA modification, which is related to the life activities of many organisms. Studying its distribution is very important to reveal its ...biological function. Previously, high-throughput sequencing was used to identify 5hmC, but it is expensive and inefficient. Therefore, machine learning is used to identify 5hmC sites. Here, we design a model called R5hmCFDV, which is mainly divided into feature representation, feature fusion and classification. (i) Pseudo dinucleotide composition, dinucleotide binary profile and frequency, natural vector and physicochemical property are used to extract features from four aspects: nucleotide composition, coding, natural language and physical and chemical properties. (ii) To strengthen the relevance of features, we construct a novel feature fusion method. Firstly, the attention mechanism is employed to process four single features, stitch them together and feed them to the convolution layer. After that, the output data are processed by BiGRU and BiLSTM, respectively. Finally, the features of these two parts are fused by the multiply function. (iii) We design the deep voting algorithm for classification by imitating the soft voting mechanism in the Python package. The base classifiers contain deep neural network (DNN), convolutional neural network (CNN) and improved gated recurrent unit (GRU). And then using the principle of soft voting, the corresponding weights are assigned to the predicted probabilities of the three classifiers. The predicted probability values are multiplied by the corresponding weights and then summed to obtain the final prediction results. We use 10-fold cross-validation to evaluate the model, and the evaluation indicators are significantly improved. The prediction accuracy of the two datasets is as high as 95.41% and 93.50%, respectively. It demonstrates the stronger competitiveness and generalization performance of our model. In addition, all datasets and source codes can be found at https://github.com/HongyanShi026/R5hmCFDV.
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